Using Collaborative Edge-Cloud Cache for Search in Internet of Things

Tang, Jine; Zhou, Zhangbing; Xue, Xiao; Wang, Gongwen

doi:10.1109/jiot.2019.2946389

Cited by 16 publications

(13 citation statements)

References 29 publications

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“…(i) Centralized: in the model training process, the data center requests training data from edge nodes to train ensemble learning models on the server (ii) P-cache: a caching mechanism in collaborative edgecloud computing architecture [31] is proposed, in which edge computing nodes periodically request cached data for submodels' learning, and data center server performs ensemble learning…”

Section: Methodsmentioning

confidence: 99%

“…rough reinforcement learning to find the appropriate cache state, Ndikumana et al [30] proposed collaborative cache allocation and computation offloading, where the MEC servers collaborate for executing computation tasks and data caching. Tang et al [31] proposed caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n-hop neighbor activity regions. Khan et al [32] proposed reversing the way in which node connectivity is used for the placement of content in caching networks and introduced a Low-Centrality High-Popularity (LoCHiP) caching algorithm that populates poorly connected nodes with popular content.…”

Section: Collaborative Cachingmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

Qin

et al. 2021

Mathematical Problems in Engineering

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To enhance the quality and speed of data processing and protect the privacy and security of the data, edge computing has been extensively applied to support data-intensive intelligent processing services at edge. Among these data-intensive services, ensemble learning-based services can, in natural, leverage the distributed computation and storage resources at edge devices to achieve efficient data collection, processing, and analysis. Collaborative caching has been applied in edge computing to support services close to the data source, in order to take the limited resources at edge devices to support high-performance ensemble learning solutions. To achieve this goal, we propose an adaptive in-network collaborative caching scheme for ensemble learning at edge. First, an efficient data representation structure is proposed to record cached data among different nodes. In addition, we design a collaboration scheme to facilitate edge nodes to cache valuable data for local ensemble learning, by scheduling local caching according to a summarization of data representations from different edge nodes. Our extensive simulations demonstrate the high performance of the proposed collaborative caching scheme, which significantly reduces the learning latency and the transmission overhead.

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Section: Methodsmentioning

confidence: 99%

Section: Collaborative Cachingmentioning

confidence: 99%

Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

Qin

et al. 2021

Mathematical Problems in Engineering

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“…Hossain et al [23] used edge computing to minimize the processing delay of IoT heterogeneous data. Tang et al [24] proposed a collaborative edge-cloud cache framework based on the STK-tree(SKIN+STK),which received search requests and cached data via an edge server. Xu et al [25] proposed a multi-object tracking algorithm, tracked target with the help of edge computing.…”

Section: Related Workmentioning

confidence: 99%

Edge and Cloud Collaborative Entity Recommendation Method towards the IoT Search

Wang

Liu

Zhang

et al. 2020

Sensors

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There are massive entities with strong denaturation of state in the physical world, and users have urgent needs for real-time and intelligent acquisition of entity information, thus recommendation technologies that can actively provide instant and precise entity state information come into being. Existing IoT data recommendation methods ignore the characteristics of IoT data and user search behavior; thus the recommendation performances are relatively limited. Considering the time-varying characteristics of the IoT entity state and the characteristics of user search behavior, an edge-cloud collaborative entity recommendation method is proposed via combining the advantages of edge computing and cloud computing. First, an entity recommendation system architecture based on the collaboration between edge and cloud is designed. Then, an entity identification method suitable for edge is presented, which takes into account the feature information of entities and carries out effective entity identification based on the deep clustering model, so as to improve the real-time and accuracy of entity state information search. Furthermore, an interest group division method applied in cloud is devised, which fully considers user’s potential search needs and divides user interest groups based on clustering model for enhancing the quality of recommendation system. Simulation results demonstrate that the proposed recommendation method can effectively improve the real-time and accuracy performance of entity recommendation in comparison with traditional methods.

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“…The benefits of introducing caching in the edge networks are obvious but there are still many challenges that should be addressed, including the cache decision and cache replacement policy 15‐20 . First, the cache decision policy should ensure that the cached file will be repeatedly requested for the next period.…”

Section: Introductionmentioning

confidence: 99%

“…And human‐like hybrid caching algorithm uses reinforcement learning to improve cache hit ratio. Tang et al 20 proposed caching mechanisms in collaborative edge‐cloud computing architecture and implemented the caching paradigm in the cloud for frequent n‐hop neighbor activity regions. Certain cache replacement policies lack consideration of differences in file sizes and are likely to result in cache pollution problems.…”

Section: Introductionmentioning

confidence: 99%

Value‐aware cache replacement in edge networks for Internet of Things

Peng

Wang

Liang

et al. 2021

Trans Emerging Tel Tech

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With the development of Internet of Things (IoT), massive amounts of data will be brought. By offloading caching from the cloud to the edge, edge caching technology represents a promising solution in the era of IoT to meet the delay requirements of IoT applications. An efficient cache decision and replacement strategy on edge caching devices is a key factor in ensuring the cache hit ratio. The existing cache replacement policies do not comprehensively consider the characteristics of cache files and are likely to result in cache pollution problems. In order to cache data reasonably, to improve the cache hit ratio, and to reduce the user request delay, we propose a concept of file cache value and a file cache value‐aware cache replacement (FCVACR) algorithm of the edge cache system in this article. File cache value is associated with three aspects of cache file: the file size, the file popularity, and the time of requests. The proposed FCVACR algorithm adopts the file cache value method, improves the utilization of edge caching space, and reduces the content transmission delay. Experimental results show that the proposed FCVACR algorithm has a higher cache hit ratio and lower user request delay than the classical cache replacement algorithms.

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Using Collaborative Edge-Cloud Cache for Search in Internet of Things

Cited by 16 publications

References 29 publications

Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

Edge and Cloud Collaborative Entity Recommendation Method towards the IoT Search

Value‐aware cache replacement in edge networks for Internet of Things

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